{"id":"https://openalex.org/W7150996445","doi":"https://doi.org/10.1109/access.2026.3681371","title":"Improving Crude Oil Price Prediction Using LLM-Based Hierarchical Semantic Extraction and Heterogeneous Graph Neural Networks","display_name":"Improving Crude Oil Price Prediction Using LLM-Based Hierarchical Semantic Extraction and Heterogeneous Graph Neural Networks","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7150996445","doi":"https://doi.org/10.1109/access.2026.3681371"},"language":"en","primary_location":{"id":"doi:10.1109/access.2026.3681371","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3681371","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3681371","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133014811","display_name":"Shang Cao","orcid":null},"institutions":[{"id":"https://openalex.org/I193524592","display_name":"Tianjin University of Finance and Economics","ror":"https://ror.org/05ev1jb90","country_code":"CN","type":"education","lineage":["https://openalex.org/I193524592"]},{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shang Cao","raw_affiliation_strings":["School of Finance, Nankai University, Jinnan District, Tianjin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Finance, Nankai University, Jinnan District, Tianjin, China","institution_ids":["https://openalex.org/I205237279","https://openalex.org/I193524592"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133006360","display_name":"Ziqi Qu","orcid":null},"institutions":[{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ziqi Qu","raw_affiliation_strings":["New York University, New York, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"New York University, New York, NY, USA","institution_ids":["https://openalex.org/I57206974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133043767","display_name":"Caichun Cen","orcid":null},"institutions":[{"id":"https://openalex.org/I111950717","display_name":"Macau University of Science and Technology","ror":"https://ror.org/03jqs2n27","country_code":"MO","type":"education","lineage":["https://openalex.org/I111950717","https://openalex.org/I4391767947"]}],"countries":["MO"],"is_corresponding":false,"raw_author_name":"Caichun Cen","raw_affiliation_strings":["Faculty of Innovation Engineering, Macau University of Science and Technology, Macau, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Innovation Engineering, Macau University of Science and Technology, Macau, China","institution_ids":["https://openalex.org/I111950717"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122128719","display_name":"Zeling Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I200769079","display_name":"Hong Kong University of Science and Technology","ror":"https://ror.org/00q4vv597","country_code":"HK","type":"education","lineage":["https://openalex.org/I200769079"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Zeling Xu","raw_affiliation_strings":["School of Engineering, Hong Kong University of Science and Technology, Hong Kong, China"],"raw_orcid":"https://orcid.org/0009-0004-3200-3860","affiliations":[{"raw_affiliation_string":"School of Engineering, Hong Kong University of Science and Technology, Hong Kong, China","institution_ids":["https://openalex.org/I200769079"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102313539","display_name":"Yuntao Zou","orcid":null},"institutions":[{"id":"https://openalex.org/I4210107865","display_name":"Wuzhou University","ror":"https://ror.org/01vv37n49","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210107865"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuntao Zou","raw_affiliation_strings":["Guangxi Key Laboratory of Machine Vision and Intelligent Control, Wuzhou University, Wuzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-8492-7684","affiliations":[{"raw_affiliation_string":"Guangxi Key Laboratory of Machine Vision and Intelligent Control, Wuzhou University, Wuzhou, China","institution_ids":["https://openalex.org/I4210107865"]}]}],"institutions":[],"countries_distinct_count":4,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.52458005,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"52911","last_page":"52926"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11059","display_name":"Market Dynamics and Volatility","score":0.4862000048160553,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11059","display_name":"Market Dynamics and Volatility","score":0.4862000048160553,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.17249999940395355,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11630","display_name":"Petroleum Processing and Analysis","score":0.040800001472234726,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/crude-oil","display_name":"Crude oil","score":0.5965999960899353},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5817000269889832},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4896000027656555},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.39340001344680786},{"id":"https://openalex.org/keywords/extraction","display_name":"Extraction (chemistry)","score":0.37790000438690186},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.3614000082015991}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8223000168800354},{"id":"https://openalex.org/C2987168347","wikidata":"https://www.wikidata.org/wiki/Q22656","display_name":"Crude oil","level":2,"score":0.5965999960899353},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5902000069618225},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5817000269889832},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4896000027656555},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4377000033855438},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43630000948905945},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.39340001344680786},{"id":"https://openalex.org/C4725764","wikidata":"https://www.wikidata.org/wiki/Q844704","display_name":"Extraction (chemistry)","level":2,"score":0.37790000438690186},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3614000082015991},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.35280001163482666},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3253999948501587},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.2736999988555908},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.2685000002384186},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.2644999921321869}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2026.3681371","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3681371","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:de691cc4a52047f69ce1a0333626898f","is_oa":true,"landing_page_url":"https://doaj.org/article/de691cc4a52047f69ce1a0333626898f","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 14, Pp 52911-52926 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3681371","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3681371","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5801805227","display_name":"\u5206\u5f62\u56fe\u50cf\u538b\u7f29\u7684\u5747\u8861\u4f18\u5316\u53ca\u8d28\u91cf\u63a7\u5236\u5173\u952e\u6280\u672f\u7814\u7a76","funder_award_id":"61961036","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5894117146","display_name":null,"funder_award_id":"2025A02","funder_id":"https://openalex.org/F4320330424","funder_display_name":"Key Laboratory of Intelligent Multimedia Technology"},{"id":"https://openalex.org/G5929012726","display_name":null,"funder_award_id":"62162054","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320330424","display_name":"Key Laboratory of Intelligent Multimedia Technology","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Crude":[0],"oil":[1,192],"price":[2,193],"forecasting":[3,194],"serves":[4],"energy":[5],"policy":[6],"formulation":[7],"and":[8,24,44,50,60,87,122,138,145,157,220],"financial":[9],"risk":[10],"management.":[11],"Existing":[12],"deep":[13],"temporal":[14,62,146],"models":[15,105],"rely":[16],"on":[17,102],"the":[18,93,126,163,186,199,208,221],"assumption":[19],"of":[20,223],"historical":[21],"pattern":[22],"continuity":[23],"respond":[25],"insufficiently":[26],"to":[27,108,153,175,185],"exogenous":[28],"events":[29,59],"such":[30],"as":[31,41,195],"geopolitical":[32],"conflicts.":[33],"Mainstream":[34],"large":[35,103],"language":[36,104],"model":[37],"approaches":[38],"encode":[39],"news":[40,110],"single":[42],"vectors,":[43],"fine-grained":[45],"semantic":[46,94],"information":[47,132],"is":[48,106,141,173],"compressed":[49],"lost":[51],"in":[52],"this":[53,76],"process.":[54],"Causal":[55],"propagation":[56],"relationships":[57],"among":[58,160],"heterogeneous":[61,131],"lag":[63],"characteristics":[64],"have":[65],"not":[66],"yet":[67],"been":[68],"incorporated":[69],"into":[70,112],"modeling.":[71],"To":[72],"address":[73],"these":[74],"limitations,":[75],"paper":[77],"proposes":[78],"three":[79],"improvements":[80],"building":[81],"upon":[82],"multimodal":[83],"graph":[84,127,133],"neural":[85],"network":[86],"Temporal":[88,209],"Fusion":[89,210],"Transformer":[90,211],"architectures.":[91],"At":[92,125,162],"extraction":[95],"level,":[96,129,166],"a":[97,130,167,196,203],"four-layer":[98],"pyramid":[99],"architecture":[100],"based":[101],"designed":[107,174],"parse":[109],"texts":[111],"structured":[113],"representations":[114],"covering":[115],"entity":[116,151],"stances,":[117],"event":[118],"types,":[119],"causal":[120,144],"chains,":[121],"market":[123,169],"sentiment.":[124],"modeling":[128],"containing":[134],"multiple":[135],"node":[136],"types":[137,140],"edge":[139],"constructed,":[142],"introducing":[143],"edges":[147],"beyond":[148],"existing":[149],"static":[150],"associations":[152],"characterize":[154],"cumulative,":[155],"propagation,":[156],"synergistic":[158],"effects":[159],"events.":[161],"feature":[164,182],"fusion":[165],"semantic-driven":[168],"state":[170],"identification":[171],"module":[172],"replace":[176],"numerical-statisticsbased":[177],"lagging":[178],"detection,":[179],"dynamically":[180],"adjusting":[181],"weights":[183],"according":[184],"identified":[187],"state.":[188],"Using":[189],"Brent":[190],"crude":[191],"validation":[197],"case,":[198],"proposed":[200],"method":[201],"achieves":[202],"17.7%":[204],"RMSE":[205],"improvement":[206,214],"over":[207],"baseline,":[212],"with":[213],"reaching":[215,228],"34.7%":[216],"during":[217],"event-intensive":[218],"periods":[219],"coefficient":[222],"determination":[224],"<italic":[225],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[226],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">R<sup>2</sup></i>":[227],"0.903.":[229]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-04-07T00:00:00"}